Biomechanics-Based User-Adaptive Variable Impedance Control for Enhanced Physical Human–Robot Interaction Using Bayesian Optimization

Abstract

This paper presents a biomechanics-based, user-adaptive variable impedance controller designed to enhance the performance of coupled human–robot systems during motion. The controller integrates the biomechanical characteristics of human limbs and dynamically adjusts the robotic impedance parameters—specifically damping, stiffness, and equilibrium trajectory—based on real-time estimations of the user's intent and direction of motion. The primary goal is to minimize the energy expenditure of the coupled human–robot system while maintaining system passivity. To address uncertainties in human behavior and noisy observations, the controller employs Bayesian optimization combined with a Gaussian process. To validate the proposed approach, human experiments are conducted using a standard robotic arm manipulator. The results demonstrate that the controller eliminates the need for manual parameter tuning, a process that is typically time-consuming. A comparative analysis against two variable impedance controllers without user-adaptive parameter adjustments reveal significant benefits, with the controller improving combined performance metrics—such as accuracy, speed, user effort, and smoothness—by over 13%. Notably, all participants in the study preferred the optimized controller over the alternatives. These findings highlight the effectiveness of the biomechanics-based, user-adaptive variable impedance control approach and its potential to enhance physical human–robot interaction in various applications that involve repetitive or continuous motion.